Several strategies have been proposed to mitigate catastrophic forgetting. A group of methods are based on regularizing the deep network weight parameters [1]}. The idea is to identify the network weights that are important for decent performance on past learned tasks, consolidate these weights according to their importance, and learn new tasks using the remaining weights that are unimportant to remember past learned tasks. The main challenge is identifying the important weights and minimize the negative effects of weight consolidation on the network learning capacity. A second group of works are based on the notion of experience replay [2]}, where the end-to-end training mechanism of deep networks is changed, rather than the network itself. The idea is to replay data points of past learned tasks along with the current task data to update the model through the pseudo-rehearsal process [3]}, i.e., retraining the model jointly on the past and the current data. Since training the network on full datasets is computationally expensive and the storage capacity is limited, only a subset of training data for the past learned tasks should be used. These samples are stored in a memory buffer of fixed maximum capacity [4]}. The main challenge is how to select these samples. For example, Schaul et al. select samples that were uncommon and led to maximum learning effects in past tasks [4]}. To alleviate the need for a memory buffer, an alternative approach is to use generative models. The idea is to enable the model to generate pseudo-data points that are similar to the data points of the past tasks and use the pseudo-data points for pseudo-rehearsal [6]}, [7]}, [8]}. We rely on memory-based experience replay to update the PAD model in our work.
